code: def test_fe_target(load_pos_and_neg_data):
      """Test custom feature engineering for target (applicable to reduced
      regression models only)
      """
      data = load_pos_and_neg_data
  
      kwargs = {
          "lag_feature": {"lag": [36, 24, 13, 12, 11, 9, 6, 3, 2, 1]}
      }
      fe_target_rr = [
          WindowSummarizer(n_jobs=1, truncate="bfill", **kwargs)
      ]
  
      # Baseline
      exp = TSForecastingExperiment()
      exp.setup(data=data, fh=12, fold=3, session_id=42)
      exp.create_model("lr_cds_dt")
      metrics1 = exp.pull()
  
      # With Feature Engineering
      exp = TSForecastingExperiment()
      exp.setup(
          data=data,
          fh=12,
          fold=3,
          fe_target_rr=fe_target_rr,
          session_id=42,
      )
      exp.create_model("lr_cds_dt")
      metrics2 = exp.pull()
  
      assert_frame_not_equal(metrics1, metrics2)
  
file_path: pycaret/tests/test_time_series_feat_eng.py
imports: import numpy
  import pytest
  from pandas.testing import assert_frame_equal
  from sktime.transformations.series.date import DateTimeFeatures
  from sktime.transformations.series.summarize import WindowSummarizer
  from time_series_test_utils import assert_frame_not_equal
  from pycaret.time_series import TSForecastingExperiment
label_desc: Use image node_id 1 for calling a global function with example usage: test_fe_target(load_pos_and_neg_data) without return types